Evidence map›Paper›PMID 36496382›Full record

ArticleMalaria journal2022

Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study.

Adelino Martins, Sereina A Herzog, Levicatus Mugenyi, Christel Faes, Niel Hens, Steven Abrams

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 41% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 4 countries.

Adelino MartinsInteruniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium.
Sereina A HerzogInstitute for Medical Informatics, Statistics and Documentation (IMI), Medical University of Graz, Graz, Austria.
Levicatus MugenyiInfectious Diseases Research Collaboration, Plot 2C Nakasero Hill road, Kampala, Uganda.
Christel FaesInteruniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium.
Niel HensInteruniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium.
Steven AbramsInteruniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium. steven.abrams@uhasselt.be.
Hasselt University · BEEduardo Mondlane University · MZInfectious Diseases Research Collaboration · UGMedical University of Graz · ATUniversity of Antwerp · BE

Funding

European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme 682540 - TransMIDSpecial Research Fund at Hasselt University BILA R-8133
6 · The paper itself

Abstract

backgroundIn spite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential in order to eliminate the disease and its burden. Malaria follow-up studies typically involve routine visits at pre-scheduled time points and/or clinical visits whenever individuals experience malaria-like symptoms. In the latter case, infection triggers outcome assessment, thereby leading to outcome-dependent sampling (ODS). Commonly used methods to analyze such longitudinal data ignore ODS and potentially lead to biased estimates of malaria-specific transmission parameters, hence, inducing an incorrect assessment and evaluation of malaria control strategies.

methodsIn this paper, a new method is proposed to handle ODS by use of a joint model for the longitudinal binary outcome measured at routine visits and the clinical event times. The methodology is applied to malaria parasitaemia data from a cohort of [Formula: see text] Ugandan children aged 0.5-10 years from 3 regions (Walukuba-300 children, Kihihi-355 children and Nagongera-333 children) with varying transmission intensities (entomological inoculation rate equal to 2.8, 32 and 310 infectious bites per unit year, respectively) collected between 2011-2014.

resultsThe results indicate that malaria parasite prevalence and force of infection (FOI) increase with age in the region of high malaria intensity with highest FOI in age group 5-10 years. For the region of medium intensity, the prevalence slightly increases with age and the FOI for the routine process is highest in age group 5-10 years, yet for the clinical infections, the FOI gradually decreases with increasing age. For the region with low intensity, both the prevalence and FOI peak at the age of 1 year after which the former remains constant with age yet the latter suddenly decreases with age for the clinically observed infections.

conclusionMalaria parasite prevalence and FOI increase with age in the region of high malaria intensity. In all study sites, both the prevalence and FOI are highest among previously asymptomatic children and lowest among their symptomatic counterparts. Using a simulation study inspired by the malaria data at hand, the proposed methodology shows to have the smallest bias, especially when consecutive positive malaria parasitaemia presence results within a time period of 35 days were considered to be due to the same infection.

Indexed as

MalariaChildCohort StudiesHumansIncidenceParasitemiaPrevalenceInterval censored dataMalaria infectionMarkov Chain Monte Carlo (MCMC)Right-truncationTime at risk

Identifiers

PMID36496382
PMCPMC9741489
OpenAlexW4311662141

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.